The goal of this research is to develop rapid and robust techniques for quantitative 3D myocardial tissue characterization using Magnetic Resonance Imaging. The ability to quantitatively distinguish between normal tissue and diseased myocardium early in disease progression in a way that allows intra- and inter-individual comparison enables timely intervention, essential for preventing long-term tissue damage. The most commonly mapped tissue properties in cardiac MRI are T1 and T2, and the recently developed biomarker extracellular volume fraction (ECV). When quantitative tissue property maps can be captured in the heart, disease severity can be more accurately assessed than when using standard MR imaging. However, at present, the collection of quantitative data in the heart is challenging. Only patients who are fortunate enough to have access to state-of-the-art imaging facilities can receive these informative quantitative MRI scans. Moreover, only patients with the ability to hold their breath and a regular cardiac rhythm can benefit from these scans, further limiting their utility even if they were available at all institutions. Building on our previous successful work in combining compressed sensing and parallel imaging, this proposal seeks to overcome these limitations through the development of a non-contrast cardiac scan which will provide T1, T2, and ECV maps in the heart using Magnetic Resonance Fingerprinting (MRF). In this proposal we will first optimize the MRF framework to enable robust and reproducible parameter mapping in the heart. Because MRF imaging will be push-button and minimal input is required from the operator, it will be possible to implement these scans even at institutions with limited technical expertise. Additionally, our preliminary results indicate that MRF can be used to separate different exchanging compartments within a single voxel, enabling the determination of intra- and extracellular volumes without injection of a contrast agent. We will also explore the ability of MRF to quantify exchange between the intracellular and extracellular space, which we believe reflects the underlying physiology of the tissue. We know of no other in vivo method that can assess these critical tissue properties in any organ, and if successful, these methods could complement or even replace ECV as a biomarker.